How Does a Brain Build a Cognitive Code?

Abstract

This article indicates how competition between afferent data and learned feedback expectancies can stabilize a developing code by buffering committed populations of detectors against continual erosion by new environmental demands. Tille gating phenomena that result lead to dynamically maintained critical peri(Jlds, and to attentional phenomena such as overshadowing in the adult. The fuillctional unit of cognitive coding is suggested to be an adaptive resonance, or amplification and ,prolongation of neural activity, that occurs when afferent data and efferent expectancies reach consensus through a matching process. The resonant state embodies the perceptual event, or attentional focus, and its amplified and sustained activities are capable of driving slow changes of long-term memor:r"' Mismatch between afferent data and efferent expectancies yields a global sulppression of activity and triggers a reset of short-term memory, as well as raJ~id parallel search and hypothesis testing for uncommitted cells. These mechanisms help to explain and predict, as manifestations of the unified theme of stable code development, positive and negative aftereffects, the McCollough effect, spatial frequency adaptation, monocular rivalry, binocular rivalry and hysteresis, pattern completion, and Gestalt switching; analgesia, partial reinforcement acquisition effect, conditioned reinforcers, underaroused versus overaroused depression; the contingent negative variation, P300, and pontoge]lliculo-occipital waves; olfactory coding, corticogeniculate feedback, matching of proprioceptive and terminal motor maps, and cerebral dominance. The psychophysiological mechanisms that unify these effects are inherently nonlinear and parallel and are inequivalent to the computer, probabilistic, and linear models currently in use.

Spikes, synchrony, and attentive learning by laminar thalamocortical circuits

“…It clarifies data about how bottom-up processing and learned tuning of adaptive filters is modulated by top-down attentive learned expectations that embody predictions or hypotheses that focus attention on expected bottom-up stimuli (Salin and Bullier, 1995;Engel et al, 2001;Gao and Suga, 1998;Krupa et al, 1999;Desimone, 1998;Ahissar and Hochstein, 2002;Hermann et al, 2004). These data support predictions of Adaptive Resonance Theory, or ART (Grossberg, 1980(Grossberg, , 2003Carpenter andGrossberg, 1987, 1993) that top-down expectations regulate predictive coding and matching and thereby help to focus attention, synchronize and gain-modulate attended feature representations, and trigger fast learning that is dynamically buffered against catastrophic forgetting. The goal of achieving fast stable learning without catastrophic forgetting is often summarized as the stability-plasticity dilemma (Grossberg, 1980).…”

“…These data support predictions of Adaptive Resonance Theory, or ART (Grossberg, 1980(Grossberg, , 2003Carpenter andGrossberg, 1987, 1993) that top-down expectations regulate predictive coding and matching and thereby help to focus attention, synchronize and gain-modulate attended feature representations, and trigger fast learning that is dynamically buffered against catastrophic forgetting. The goal of achieving fast stable learning without catastrophic forgetting is often summarized as the stability-plasticity dilemma (Grossberg, 1980). Recent ART models, called LAMINART, have begun to show how ART predictions may be embodied in laminar cortical circuits (Grossberg, 1999(Grossberg, , 2003Raizada and Grossberg, 2003).…”

“…The SMART model clarifies how a match at the specific first-order and higher-order thalamic nuclei may induce fast learning and stable memory of neural representations in the thalamocortical system (cf., Gove et al, 1995;Grossberg, 1980Grossberg, , 2003. Such a match may occur, for example, at LGN cells in response to bottom-up driving retinal inputs and top-down modulatory expectations from layer 6 of cortical area V1 (Sillito et al, 1994).…”

“…Layer 6 I , in turn, contrast-normalizes layer 4 cell activities in response to bottom-up input patterns (Grossberg, 1980;Heeger, 1992) via a modulatory on-center, driving off-surround network (Carpenter and Grossberg, 1987;Grossberg, 1980Grossberg, , 2003 whose offsurround is mediated by layer 4 inhibitory interneurons (Grieve and Sillito, 1991). The direct pathway from LGN to layer 4 enables the cortex to fire despite the modulatory nature of the on-center from layer 6 I to 4.…”

Distributed Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks

“…The theory of adaptive resonance began with an analysis of human cognitive information processing and stable coding in a complex input environment (Grossberg, 1976a(Grossberg, , 1980. ART neural network models have added a series of new principles to the original theory and have realized these principles as quantitative systems that can be applied to problems of category learning, recognition, and prediction.…”